We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
An Efficient Deep Learning-based Intrusion Detection System for Internet of Things Networks with Hybrid Feature Reduction and Data Balancing Techniques.
- Authors
Karamollaoğlu, Hamdullah; Doğru, İbrahim Alper; Yücedağ, İbrahim
- Abstract
With the increasing use of Internet of Things (IoT) technologies, cyber-attacks on IoT devices are also increasing day by day. Detecting attacks on IoT networks before they cause any damage is crucial for ensuring the security of the devices on these networks. In this study, a novel Intrusion Detection System (IDS) was developed for IoT networks. The IoTID20 and BoT-IoT datasets were utilized during the training phase and performance testing of the proposed IDS. A hybrid method combining the Principal Component Analysis (PCA) and the Bat Optimization (BAT) algorithm was proposed for dimensionality reduction on the datasets. The Synthetic Minority Over-Sampling Technique (SMOTE) was used to address the problem of data imbalance in the classes of the datasets. The Convolutional Neural Networks (CNN) model, a deep learning method, was employed for attack classification. The proposed IDS achieved an accuracy rate of 99.97% for the IoTID20 dataset and 99.98% for the BoT-IoT dataset in attack classification. Furthermore, detailed analyses were conducted to determine the effects of the dimensionality reduction and data balancing models on the classification performance of the proposed IDS.
- Subjects
DEEP learning; INTRUSION detection systems (Computer security); DATA reduction; INTERNET of things; CONVOLUTIONAL neural networks; PRINCIPAL components analysis; COMPUTER network security
- Publication
Information Technology & Control, 2024, Vol 53, Issue 1, p243
- ISSN
1392-124X
- Publication type
Article
- DOI
10.5755/j01.itc.53.1.34933